Ai

A week ago I would have told you the open source community rejected AI for four obvious reasons. Then I tried defending each one properly. Most of them collapse under scrutiny — and what’s left is a much more tractable conversation. The conventional answer Every maintainer I know has roughly the same three or four complaints about AI tooling. First, nobody can tell where the training data came from, which sits badly with a culture obsessed with provenance. Second, maintainers are drowning in AI-generated PRs and CVE reports — Daniel Stenberg’s posts on the curl side of this are now the canonical example. Third, AI shortcuts the old apprenticeship loop where contributors learned a codebase by earning review trust over months. And fourth, the power asymmetry: frontier models need hyperscaler capital, which sits uneasily inside a movement built on “you can read, modify, and redistribute the thing.” ...

What Extreme Programming taught us about collaboration — and why it maps perfectly onto working with LLMs The current conversation is missing something If you spend any time in developer circles right now, the conversation about AI coding tools tends to collapse into one of two camps. The first camp believes we are months away from AI replacing software developers entirely — that the role of the human is already vestigial, a temporary inconvenience on the road to full automation. The second camp pushes back hard, arguing that AI is little more than a sophisticated autocomplete — useful for boilerplate, dangerous if trusted, and nowhere near capable of producing anything a senior engineer couldn’t do faster with a clear head and a good keyboard shortcut. ...